# Computations
import numpy as np
import pandas as pd

# scipy
import scipy.stats as stats
from scipy.stats import norm

# sklearn
from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score
from sklearn.metrics import plot_confusion_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, cross_val_score, KFold
from sklearn.feature_selection import RFE
from sklearn import metrics
from sklearn.utils.fixes import loguniform

from sklearn.ensemble import RandomForestClassifier

# Text
from colorama import Fore, Back, Style
import re

# Visualisation libraries
import seaborn as sns

import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec

import missingno as msno

from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from wordcloud import WordCloud
from plotly.subplots import make_subplots
import plotly.express as px

# Graphics in retina format 
%config InlineBackend.figure_format = 'retina' 

# sns setting
sns.set_context("paper", rc={"font.size":12,"axes.titlesize":14,"axes.labelsize":12})
sns.set_style("whitegrid")

# plt setting
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

Telco Customer Churn (Classification)

In this article, we would like to predict customer churn for Telco Customer Churn data.

Table of Contents

• Initial Analysis
• Preprocessing
• Modeling
• Final Thoughts

Dataset

• Customers who left within the last month – the column is called Churn
• Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device * protection, tech support, and streaming TV and movies
• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
• Demographic info about customers – gender, age range, and if they have partners and dependents

Columns	Description
customerID	Customer ID
gender	Whether the customer is a male or a female
SeniorCitizen	Whether the customer is a senior citizen or not (1, 0)
Partner	Whether the customer has a partner or not (Yes, No)
Dependents	Whether the customer has dependents or not (Yes, No)
tenure	Number of months the customer has stayed with the company
PhoneService	Whether the customer has a phone service or not (Yes, No)
MultipleLines	Whether the customer has multiple lines or not (Yes, No, No phone service)
InternetService	Customer’s internet service provider (DSL, Fiber optic, No)
OnlineSecurity	Whether the customer has online security or not (Yes, No, No internet service)
OnlineBackup	Whether the customer has an online backup or not (Yes, No, No internet service)
DeviceProtection	Whether the customer has device protection or not (Yes, No, No internet service)
TechSupport	Whether the customer has tech support or not (Yes, No, No internet service)
StreamingTV	Whether the customer has streaming TV or not (Yes, No, No internet service)
StreamingMovies	Whether the customer has streaming movies or not (Yes, No, No internet service)
Contract	The contract term of the customer (Month-to-month, One year, Two years)
PaperlessBilling	Whether the customer has paperless billing or not (Yes, No)
PaymentMethod	The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
MonthlyCharges	The amount charged to the customer monthly
TotalCharges	The total amount charged to the customer
Churn	Whether the customer churned or not (Yes or No)

Data = pd.read_csv('telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')

def Data_info(Inp, Only_NaN = False):
    Out = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = Out.join(Inp.isnull().sum().to_frame(name = 'Number of NaN Values'), how='outer')
    Out['Percentage'] = np.round(100*(Out['Number of NaN Values']/Inp.shape[0]),2)
    if Only_NaN:
        Out = Out.loc[Out['Number of NaN Values']>0]
    return Out

def dtypes_group(Inp):
    Temp = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = pd.DataFrame(index =Temp['Data Type'].unique(), columns = ['Columns'])
    for c in Temp['Data Type'].unique():
        Out.loc[Out.index == c, 'Columns'] = [Temp.loc[Temp['Data Type'] == c].index.tolist()]
    return Out

_ = msno.bar(Data, figsize=(16,5), fontsize=14, log=False, color="#34495e")

def text_sep(txt): return re.sub(r"(\w)([A-Z])", r"\1 \2", txt)

def col_details(Col):
    print(Back.BLACK + Fore.CYAN + Style.NORMAL + '%s:' % text_sep(Col))
    print(Style.RESET_ALL)
    print('%s' % ', '.join(Data[Col].unique()))
    
Data.rename(columns = {'gender':'Gender', 'tenure':'Tenure'}, inplace = True)
Data.columns = [text_sep(txt) for txt in Data.columns.tolist()]

Initial Analysis

Churn Percentage by Gender

Feature = 'Gender'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
display(Temp)
C = ['aquamarine', 'steelblue']
SC = 'Navy'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 220)
fig.update_traces(marker_line_color= SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title = 'Churn Percentage by Gender', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color= SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Not Churned', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Gender	Churn
Female	No	2549	36.19
	Yes	939	13.33
Male	No	2625	37.27
	Yes	930	13.20

It can be seen that there is a balance between the two genders among churned customers.

Churn Percentage by Senior Citizen

Temp = Data.copy()
Temp['Senior Citizen'] = Temp['Senior Citizen'].map(lambda x: 'Yes' if x ==1 else 'No')
Feature = 'Senior Citizen'
Temp = Temp.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
display(Temp)
C = ['greenyellow', 'seagreen']
SC = 'DarkGreen'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 220)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title = 'Churn Percentage by Gender', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color=SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Not Churned', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Senior Citizen	Churn
No	No	4508	64.01
	Yes	1393	19.78
Yes	No	666	9.46
	Yes	476	6.76

It can be seen that only 25.5% of the senior customers were churned.

Churn Percentage by Partner

Feature = 'Partner'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

display(Temp)
C = ['bisque', 'orange']
SC = 'DarkOrange'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 220)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title = 'Churn Percentage by Gender', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color=SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Not Churned', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Partner	Churn
No	No	2441	34.66
	Yes	1200	17.04
Yes	No	2733	38.80
	Yes	669	9.50

Over 64% of churned customers did not have any partners.

Churn Percentage by Dependents

Feature = 'Dependents'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

display(Temp)
C = ['pink', 'hotpink']
SC = 'DarkRed'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 220)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title = 'Churn Percentage by Gender', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color=SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Not Churned', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Dependents	Churn
No	No	3390	48.13
	Yes	1543	21.91
Yes	No	1784	25.33
	Yes	326	4.63

Over 82 percent of churned customers did not have any dependents.

Churn Percentage by Tenure

Feature = 'Tenure'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage',color_continuous_scale= 'ylgn', height= 450)
fig.show()


C = ['violet', 'mediumorchid']
fig = px.bar(Temp, x= Feature, y= 'Percentage',
             color = 'Churn', text = 'Percentage', color_discrete_sequence= C, height= 500)

fig.update_traces(marker_line_color='Indigo', marker_line_width=1.2, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide')
fig['layout']['yaxis'].update(range=[0, 10])
fig.update_layout(title = 'Churn Percentage by Gender', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()
del Feature

Customers with a higher tenure tend to churn less.

Churn Percentage by Contract

Feature = 'Contract'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

display(Temp)
C = ['greenyellow', 'limeGreen','DarkGreen']
SC = 'DarkGreen'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 240)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title = 'Churn Percentage by Gender', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color= SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Not Churned', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Contract	Churn
Month-to-month	No	2220	31.52
	Yes	1655	23.50
One year	No	1307	18.56
	Yes	166	2.36
Two year	No	1647	23.38
	Yes	48	0.68

The majority of churned customers were on a month-to-month base contract.

Churn Percentage by Payment Method

Feature = 'Payment Method'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

display(Temp)
C = ['azure','paleturquoise','steelblue','MidnightBlue']
SC = 'Navy'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 260)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title = 'Churn Percentage by Gender', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color= SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Not Churned', x=0.85, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Payment Method	Churn
Bank transfer (automatic)	No	1286	18.26
	Yes	258	3.66
Credit card (automatic)	No	1290	18.32
	Yes	232	3.29
Electronic check	No	1294	18.37
	Yes	1071	15.21
Mailed check	No	1304	18.51
	Yes	308	4.37

Customers with an automatic payment method churned less.

Preprocessing

Int Columns

Data_types = dtypes_group(Data)
display(Data_types)
Temp = Data_types.loc[Data_types.index == 'int64'].values[0,0]
Data[Temp] = Data[Temp].astype(int)
del Temp

	Columns
int64	[Senior Citizen, Tenure]
float64	[Monthly Charges]
object	[customer ID, Payment Method, Paperless Billin...

Float Columns

Temp = Data_types.loc[Data_types.index == 'float64'].values[0,0]
Data[Temp] = Data[Temp].astype(float)
del Temp
Data['Total Charges'] = pd.to_numeric(Data['Total Charges'], errors='coerce')

Yes/No Columns

First, let's convert all Yes/No columns using as follows

\begin{cases} 0 &\mbox{No}\\ 1 &\mbox{Yes}\end{cases}

Temp = []
for i in Data_types.loc[Data_types.index == 'object'].values[0,0]:
    if set(Data[i].unique().tolist()) == {'No', 'Yes'}:
        Temp.append(i)
        
Data[Temp] = Data[Temp].replace({'Yes':1, 'No':0}).astype(int)
del Temp

However, some other columns can be converted similarly; however, we need to create a new feature.

Temp = []
for i in Data_types.loc[Data_types.index == 'object'].values[0,0]:
    if set(Data[i].unique().tolist()) == {'No', 'No internet service', 'Yes'}:
        Temp.append(i)
        
print('Columns: %s' %', '.join(Temp))

Columns: Streaming Movies, Streaming TV, Tech Support, Device Protection, Online Backup, Online Security

Note that,

col_details('Internet Service')

Internet Service:

DSL, Fiber optic, No

This Column can be coded as follows

$$\mbox{InternetServiceType} = \begin{cases} 0 &\mbox{No} \\ 1 &\mbox{DSL}\\ 2 &\mbox{Fiber optic}\end{cases}$$

def myfun(x):
    if x == 'No':
        return 0
    elif x == 'DSL':
        return 1
    else:
        return 2

Data['Internet Service'] = Data['Internet Service'].apply(lambda x: myfun(x)).astype(int)
del myfun

Since we have already included No interent service in InternetService, we can code the rest as,

\begin{cases} 0 &\mbox{No, No internet service}\\ 1 &\mbox{Yes}\end{cases}

Data[Temp] = Data[Temp].applymap(lambda x: 1 if x =='Yes' else 0).astype(int)

Since, there is already a feature as PhoneService, for MultipleLines, we can try $$ \mbox{MultipleLines} = \begin{cases} 0 &\mbox{No, No phone service}\\ 1 &\mbox{Yes}\end{cases} $$

Data['Multiple Lines'] = Data['Multiple Lines'].map(lambda x: 1 if x =='Yes' else 0).astype(int)

Other Columns

Data_types = dtypes_group(Data)
Temp = Data_types.loc[Data_types.index == 'object'].values[0,0]
print('Columns: %s' %', '.join(Temp))

Columns: Contract, Gender, Payment Method, customer ID

Contract

col_details('Contract')

Contract:

Month-to-month, One year, Two year

\begin{cases} 0 &\mbox{Month-to-month}\\ 1 &\mbox{One year}\\ 2 &\mbox{Two year} \end{cases}

Data['Contract'] = Data['Contract'].replace({'Month-to-month':0, 'One year':1, 'Two year':2}).astype(int)

Gender

$$ \mbox{Gender} = \begin{cases} 0 &\mbox{Female}\\ 1 &\mbox{Male}\end{cases} $$

Data['Gender'] = Data['Gender'].map(lambda x: 1 if x =='Male' else 0).astype(int)

PaymentMethod

col_details('Payment Method')

Payment Method:

Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic)

In this case, we can not rank these values. Therefore,

Data = Data.join(pd.get_dummies(Data['Payment Method']).astype(int))
Data = Data.drop(columns = ['Payment Method'])
Data_types = dtypes_group(Data)
display(Data_types)

	Columns
int32	[Device Protection, Credit card (automatic), B...
float64	[Monthly Charges, Total Charges]
object	[customer ID]

Data Correlations

Let's take a look at the variance of the features.

display(Data.drop(columns = ['Churn']).var().sort_values(ascending = False).to_frame(name= 'Variance')\
       .style.background_gradient(cmap=sns.light_palette("green", as_cmap=True)).set_precision(2))

	Variance
Total Charges	5138252.41
Monthly Charges	905.41
Tenure	603.17
Contract	0.70
Internet Service	0.61
Gender	0.25
Partner	0.25
Multiple Lines	0.24
Paperless Billing	0.24
Streaming Movies	0.24
Streaming TV	0.24
Online Backup	0.23
Device Protection	0.23
Electronic check	0.22
Dependents	0.21
Tech Support	0.21
Online Security	0.20
Mailed check	0.18
Bank transfer (automatic)	0.17
Credit card (automatic)	0.17
Senior Citizen	0.14
Phone Service	0.09

def Correlation_Plot (Df,Fig_Size):
    Correlation_Matrix = Df.corr().round(2)
    mask = np.zeros_like(Correlation_Matrix)
    mask[np.triu_indices_from(mask)] = True
    for i in range(len(mask)):
        mask[i,i]=0
    Fig, ax = plt.subplots(figsize=(Fig_Size,Fig_Size))
    sns.heatmap(Correlation_Matrix, ax=ax, mask=mask, annot=True, square=True, 
                cmap =sns.color_palette("Greens", n_colors=10), linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": .6})

Correlation_Plot (Data, 16)

Correlations of features with customer Churn.

Temp = Data.corr().round(2)
Temp['Churn'].sort_values().to_frame(name= 'Correlation')[:-1].style.background_gradient(cmap='RdYlGn',
                                                                                 subset=['Correlation']).set_precision(2)

	Correlation
Contract	-0.40
Tenure	-0.35
Total Charges	-0.20
Online Security	-0.17
Tech Support	-0.16
Dependents	-0.16
Partner	-0.15
Credit card (automatic)	-0.13
Bank transfer (automatic)	-0.12
Mailed check	-0.09
Online Backup	-0.08
Device Protection	-0.07
Gender	-0.01
Phone Service	0.01
Multiple Lines	0.04
Streaming TV	0.06
Streaming Movies	0.06
Senior Citizen	0.15
Paperless Billing	0.19
Monthly Charges	0.19
Electronic check	0.30
Internet Service	0.32

fig, ax = plt.subplots(nrows=1, ncols=3, figsize = (16, 6))
Temp = ['Tenure','Monthly Charges','Total Charges']

for i in range(len(Temp)):
    _ = sns.distplot(Data[Temp[i]], 
                     fit=norm, kde=False, color='seagreen', ax= ax[i])

Modeling

df = Data.drop(columns = ['customer ID'])
df = df.fillna(df.mean())
Target = 'Churn'
X = df.drop(columns = [Target])
y = df[Target]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
pd.DataFrame(data={'Set':['X_train','X_test','y_train','y_test'],
               'Shape':[X_train.shape, X_test.shape, y_train.shape, y_test.shape]}).set_index('Set').T

Set	X_train	X_test	y_train	y_test
Shape	(4930, 22)	(2113, 22)	(4930,)	(2113,)

Furthermore, we would like to standardize features by removing the mean and scaling to unit variance. In this article, we demonstrated the benefits of scaling data using StandardScaler().

scaler = StandardScaler()

X_train_STD = scaler.fit_transform(X_train)
X_test_STD = scaler.transform(X_test)

X_train_STD = pd.DataFrame(data = X_train_STD, columns = X_train.columns)
X_test_STD = pd.DataFrame(data = X_test_STD, columns = X_test.columns)

A number of functions that we would use.

def Performance(clf, X_test = X_test_STD):
    df = pd.DataFrame()
    y_pred = clf.predict(X_test)
    df = df.append({'Score': clf.score(X_test, y_test),
                    'F1 Score': f1_score(y_test.values, y_pred, average= 'weighted'),
                    'Precision Score': precision_score(y_test.values, y_pred, average= 'weighted'),
                    'Recall Score':  recall_score(y_test.values, y_pred, average= 'weighted')}, ignore_index=True)
    display(df.style.hide_index())

def highlight_max(s):
    is_max = s == s.max()
    return ['background-color: SpringGreen' if v else '' for v in is_max]


def Feature_Ranking(clf):
    df = pd.DataFrame()
    for n in range(2, X.shape[1]):
        selector = RFE(estimator= clf, n_features_to_select=n, verbose=0)
        selector.fit(X_train_STD, y_train)
        df = df.append({'Number of Features to Select': n,
                        'Score':metrics.accuracy_score(y_test, selector.predict(X_test_STD)),
                        'Features': X.columns[selector.support_].tolist(),
                        'Best Features':X.columns[selector.ranking_ == 1].tolist()}, ignore_index=True)

    df = df[['Number of Features to Select', 'Score', 'Features', 'Best Features']]
    display(df.style.apply(highlight_max, subset=['Score']))
    return df.loc[df.Score == df.Score.max(), 'Features'].values[0]

def ROC_Curve(clf, X_test = X_test_STD):
    # false positive rates, true positive rates and thresholds
    fpr, tpr, threshold = metrics.roc_curve(y_test, clf.predict_proba(X_test)[:,1])

    fig, ax = plt.subplots(1, 1, figsize=(5.5, 5.5))
    _ = ax.plot(fpr, tpr, lw=2, label = 'AUC = %0.2f' % metrics.auc(fpr, tpr))
    _ = ax.plot([0, 1], [0, 1],'r--', lw=2)
    _ = ax.legend(loc = 'lower right', fontsize = 14)
    _ = ax.set_xlim([0,1])
    # _ = ax.set_ylim([0,1])
    _ = ax.set_xlabel('False Positive Rate (FPR)')
    _ = ax.set_ylabel('True Positive Rate (TPR)')

For this set of data, we would like to implement Random Forest Classifier. which creates a set of decision trees from a randomly selected subset of the training set. It benefits from voting of different decision trees for classifying the final outputs.

rfc = RandomForestClassifier()
_ = rfc.fit(X_train_STD,y_train)
Performance(rfc)
ROC_Curve(rfc)

F1 Score	Precision Score	Recall Score	Score
0.780087	0.779921	0.792239	0.792239

However, we only need to implement features that are useful for classifications. In this article, we demonstrated the importance of feature ranking.

Best_Features = Feature_Ranking(rfc)

	Number of Features to Select	Score	Features	Best Features
0	2.000000	0.771415	['Monthly Charges', 'Total Charges']	['Monthly Charges', 'Total Charges']
1	3.000000	0.769522	['Tenure', 'Monthly Charges', 'Total Charges']	['Tenure', 'Monthly Charges', 'Total Charges']
2	4.000000	0.769995	['Tenure', 'Contract', 'Monthly Charges', 'Total Charges']	['Tenure', 'Contract', 'Monthly Charges', 'Total Charges']
3	5.000000	0.778987	['Tenure', 'Internet Service', 'Contract', 'Monthly Charges', 'Total Charges']	['Tenure', 'Internet Service', 'Contract', 'Monthly Charges', 'Total Charges']
4	6.000000	0.776621	['Tenure', 'Internet Service', 'Contract', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Tenure', 'Internet Service', 'Contract', 'Monthly Charges', 'Total Charges', 'Electronic check']
5	7.000000	0.776621	['Tenure', 'Internet Service', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Tenure', 'Internet Service', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
6	8.000000	0.777567	['Gender', 'Tenure', 'Internet Service', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Tenure', 'Internet Service', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
7	9.000000	0.783247	['Gender', 'Partner', 'Tenure', 'Internet Service', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Partner', 'Tenure', 'Internet Service', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
8	10.000000	0.782773	['Gender', 'Partner', 'Tenure', 'Internet Service', 'Online Security', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Partner', 'Tenure', 'Internet Service', 'Online Security', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
9	11.000000	0.792712	['Gender', 'Partner', 'Tenure', 'Internet Service', 'Online Security', 'Online Backup', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Partner', 'Tenure', 'Internet Service', 'Online Security', 'Online Backup', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
10	12.000000	0.790345	['Gender', 'Partner', 'Tenure', 'Internet Service', 'Online Security', 'Online Backup', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Partner', 'Tenure', 'Internet Service', 'Online Security', 'Online Backup', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
11	13.000000	0.793185	['Gender', 'Partner', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Partner', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
12	14.000000	0.792239	['Gender', 'Senior Citizen', 'Partner', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Senior Citizen', 'Partner', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
13	15.000000	0.788452	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
14	16.000000	0.794132	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
15	17.000000	0.794605	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
16	18.000000	0.793658	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Electronic check']
17	19.000000	0.797918	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Credit card (automatic)', 'Electronic check']	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Credit card (automatic)', 'Electronic check']
18	20.000000	0.797444	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Credit card (automatic)', 'Electronic check', 'Mailed check']	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Credit card (automatic)', 'Electronic check', 'Mailed check']
19	21.000000	0.795551	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Bank transfer (automatic)', 'Credit card (automatic)', 'Electronic check', 'Mailed check']	['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Bank transfer (automatic)', 'Credit card (automatic)', 'Electronic check', 'Mailed check']

Thus, the best features:

print(Best_Features)

['Gender', 'Senior Citizen', 'Partner', 'Dependents', 'Tenure', 'Multiple Lines', 'Internet Service', 'Online Security', 'Online Backup', 'Device Protection', 'Tech Support', 'Streaming TV', 'Streaming Movies', 'Contract', 'Paperless Billing', 'Monthly Charges', 'Total Charges', 'Credit card (automatic)', 'Electronic check']

rfc = RandomForestClassifier()
_ = rfc.fit(X_train_STD[Best_Features],y_train)
Performance(rfc, X_test_STD[Best_Features])
ROC_Curve(rfc, X_test_STD[Best_Features])

F1 Score	Precision Score	Recall Score	Score
0.783356	0.784129	0.796025	0.796025

The main classification metrics:

pd.DataFrame(classification_report(y_test,rfc.predict(X_test[Best_Features]),
                                   output_dict = True, target_names = ['No_Churn','Churn']))

	No_Churn	Churn	accuracy	macro avg	weighted avg
precision	0.728945	0.297872	0.719356	0.513409	0.611843
recall	0.978558	0.024390	0.719356	0.501474	0.719356
f1-score	0.835506	0.045089	0.719356	0.440297	0.620788
support	1539.000000	574.000000	0.719356	2113.000000	2113.000000

A confusion matrix allows the visualization of the performance of a classification model.

fig, ax = plt.subplots(1, 1, figsize=(6, 6))
_ = plot_confusion_matrix(rfc, X_test_STD[Best_Features], y_test, display_labels= ['No_Churn','Churn'],
                      cmap= "Greens", normalize= 'true', ax = ax)
_ = ax.set_title('Normalized Confusion Matrix')

Final Thoughts

Finally, customer churn can be predicted using our model for the test data.

display(pd.DataFrame(list(rfc.predict_proba(X_test_STD[Best_Features].values)), columns=['No_Churn','Churn']))

	No_Churn	Churn
0	0.39	0.61
1	0.88	0.12
2	1.00	0.00
3	0.39	0.61
4	1.00	0.00
...	...	...
2108	0.09	0.91
2109	0.44	0.56
2110	0.95	0.05
2111	0.73	0.27
2112	0.85	0.15

2113 rows × 2 columns

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